DocuMed

Inspiration 🌟

The challenges faced by the healthcare industry in managing vast amounts of medical data and simplifying communication between healthcare professionals and equipment manufacturers served as the driving force behind the creation of DocuMed. DocuMed was developed to Implement AI to predict equipment needs based on historical data. Automatically generate purchase orders and contracts for medical equipment vendors, which can be signed digitally through Dropbox Sign API.

What it Does 🚀

DocuMed is a Streamlit web application designed to address the following key functions:

• 💼 Medical Data Summarization: DocuMed leverages the LaMini-Flan-T5-248M model from Hugging Face Transformers to analyze and provide concise and detailed summaries of extensive medical data input by users.

• 📄 Contract Generation: With just a single click, DocuMed uses Dropbox API to generate contracts and PyPDF to merge it with the compiled data. These contracts are securely stored in Dropbox.

• 📧 Facilitated Communication: DocuMed streamlines communication by sending these completed contracts to specified recipients, often medical equipment manufacturers, through email, utilizing the Dropbox API. This simplified process ensures effective and secure communication.

How We Built It 🛠️

DocuMed was constructed using a combination of technologies and components, including:

• 💻 Streamlit: We developed the user interface using Streamlit, creating a user-friendly and interactive experience.

• 🤖 Hugging Face Transformers: The core of the project relies on the LaMini-Flan-T5-248M model from Hugging Face Transformers, enabling accurate data summarization.

• 📝 PyPDF: We employed PyPDF to generate contracts from the summarized data, ensuring the production of professional and standardized documents.

• ☁️ Dropbox API: Integration with the Dropbox API was implemented to securely manage documents, enabling document storage and facilitating email communication.

Challenges We Ran Into ⚙️

The project posed several challenges, including:

• 🔒 Data Security: Ensuring the protection of sensitive patient data and verifying the legal validity of digital signatures required stringent security and privacy measures.

• ⚡ Memory Consumption: One significant challenge was the substantial memory consumption during the initialization of the LaMini-Flan-T5-248M model. Loading the model's weights into memory demanded a significant amount of RAM, impacting the application's overall performance. To overcome this challenge, we implemented an offload folder strategy. We moved the model's weights to a separate folder, allowing more efficient loading when needed, reducing memory usage, and enhancing application responsiveness.

Accomplishments We're Proud Of 🏆

Our achievements in the project include:

• ✅ Full Automation: Accomplishing full automation in summarization, contract generation, and document management processes.

• 🎨 User-Friendly Interface: Designing a user-friendly interface that simplifies complex tasks for healthcare providers, ensuring a smooth user experience.

• 🔒 Secure Communication: Ensuring secure and compliant communication between healthcare providers and equipment manufacturers, enhancing trust and confidentiality.

What We Learned 📚

Through the development of DocuMed, we gained valuable insights into:

• 🧠 AI-Based Summarization Models: We deepened our understanding of AI-based summarization models, enabling us to implement accurate data summarization.

• 🔗 API Utilization: We effectively harnessed APIs for document management and email communication, streamlining these critical aspects of the application.

• 💻 Web Application Development: We honed our skills in web application development using Streamlit, creating an intuitive and efficient interface.

What's Next for DocuMed 🚀

Looking ahead, we have several plans for the continued development of DocuMed, including:

• 📈 Scaling Up: Expanding the application to accommodate a broader user base, catering to more healthcare institutions.

• ⚙️ Customization: Allowing users to tailor the application to meet their specific needs, enhancing its flexibility and utility.

• 🧠 Machine Learning Integration: Implementing machine learning techniques for even more accurate data summarization and prediction.

• 👥 Collaboration Features: Introducing collaboration features to facilitate teamwork among healthcare providers, further improving efficiency and communication.

Built With

• dropbox
• huggingface
• pypdf
• streamlit
• transformers